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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Electronic structures and binding motifs of sodium polysulfide clusters NaSn- (n = 5-9): a joint negative ion photoelectron spectroscopy and computational investigation

Motivated by the significant biological functions of polysulfides in life processes and important applications of sodium polysulfides in battery sciences, herein we report a joint experimental and computational study on the electronic and geometric structures of a series of NaSn- (n = 5-9) clusters. The cryogenic, size-selective, negative ion photoelectron spectroscopy (NIPES) is employed to obtain their photoelectron spectra, in which distinctive spectral features with electron binding energy (EBE) up to 6.4 eV are unraveled. The EBE of the first peak in each spectrum for NaSn- (n =5-9), assigned to the transition from the ground state of anion to the ground state of each neutral radical, is observed to increase with cluster size. The vertical detachment energies (VDEs), determined from the first peak maximum, are 3.43 ± 0.02, 3.57 ± 0.02, 3.82 ± 0.03, 3.86 ± 0.02, and 4.00 ± 0.02 eV and the adiabatic detachment energies (ADEs), determined by the onset of the first peak, are 3.27 ± 0.05, 3.44 ± 0.05, 3.65 ± 0.05, 3.75 ± 0.05, and 3.93 ± 0.05 eV, for n = 5-9, respectively. A plural of low-lying isomers of anions are screened and identified with density functional theory calculations, showing that all featuring preference for a chain-like polysulfide moiety electrostatically interacted with a sodium cation. The calculated VDEs and ADEs using the high-level CCSD(T)/aug-cc-pVTZ method are in excellent agreement with experimental results, further confirming the identified isomers as most stable ones. Further analyses based on the excited-state transition, molecular orbitals and natural population charge are performed to assign all observed spectral bands along the binding energy axis, suggesting that the electron detachment process and observed excitations mainly derived from the polysulfide chain of NaSn- clusters. This work provides a fundamental understanding of the intrinsic molecular properties of sodium polysulfide systems, which widely exist in life science and sodium-sulfur cells.

Li, Zhipeng↗
NASA NTRS1992

The distribution of cD galaxy peculiar velocities

A modified version of a cluster evolution computer code first described in Richstone and Malumuth (1983) and Malumuth and Richstone (1984) to investigate the distributon of cD galaxy peculiar velocities and positions relative to the center, for a sample of cDs formed through mergers in a virialized cluster. This distribution is compared to an observed distribution of 19 cD galaxies to test whether cD galaxies with large peculiar velocities could have been formed in virialized clusters. At the time of their formation the cD galaxies have a distribution of peculiar velocities similar to that of the observed cD galaxies. By the end of the simulations the cD galaxies were dragged to the center of the cluster by dynamical friction, and the distribution of peculiar velocities is inconsistent with the observed distribution. The typical cD galaxy has nine mergers and is 5 solar luminosities. This is in comparison with the observed luminosity of Morgan poor clusters of 7 solar luminosities.

Malumuth, Eliot M.↗
DOE OSTI2018

ShareAnalytics

ShareAnalytics is a lightweight extensible platform for collaborative data analytics and facility characterization via Non-intrusive Load Monitoring (NILM). It consists of three core components: a full stack web application, a dashboard tool for analyzing streaming data and a High Performance Computing (HPC) cluster leveraging slurm for resource management to perform real time analysis. The entire framework is implemented in Docker for ease of deployment and to allow the user to test the platform before making a significant investment in hardware if they require additional computing resources. While many other platforms for analyzing streaming data exists, ShareAnalytics is unique in that HPC capabilities and facility characterization via scored analytics are fully integrated into the platform.

Oesch, TimothyS↗
NASA NTRS1999

Climate Ocean Modeling on a Beowulf Class System

With the growing power and shrinking cost of personal computers, the availability of fast ethernet interconnections, and public domain software packages, it is now possible to combine them to build desktop parallel computers at a fraction of what it would cost to buy systems of comparable power from supercomputer companies.

Beowulf↗
DOE OSTI2020

A Vertex-Aligned Model for Packing 4-Hexagonal Clusters in a Regular Hexagonal Container

This paper deals with a problem the packing polyhex clusters in a regular hexagonal container. It is a common problem in many applications with various cluster shapes used, but symmetric polyhex is the most useful in engineering due to its geometrical properties. Hence, we concentrate on mathematical modeling in such an application, where using the “bee” tetrahex is chosen for the new Compact Muon Solenoid (CMS) design upgrade, which is one of four detectors used in Large Hadron Collider (LHC) experiment at European Laboratory for Particle Physics (CERN). We start from the existing hexagonal containers with hexagonal cells packed inside, and uniform clustering applied. We compare the center-aligned (CA) and vertex-aligned (VA) models, analyzing cluster rotations providing the increased packing efficiency. We formally describe the geometrical properties of clustering approaches and show that cluster sharing is inevitable at the container border with uniform clustering. In addition, we propose a new vertex-aligned model decreasing the number of shared clusters in the uniform scenario, but with a smaller number of clusters contained inside the container. Also, we describe a non-uniform tetrahex cluster packing scheme in the proposed container model. With the proposed cluster packing solution, it is accomplished that all clusters are contained inside the container region. Since cluster-sharing is completely avoided at the container border, the maximal packing efficiency is obtained compared to the existing models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Can mesoscale models capture the effect from cluster wakes offshore?

Long wakes from offshore wind turbine clusters can extend tens of kilometers downstream, affecting the wind resource of a large area. Given the ability of mesoscale numerical weather prediction models to capture important atmospheric phenomena and mechanisms relevant to wake evolution, they are often used to simulate wakes behind large wind turbine clusters and their impact over a wider region. Yet, uncertainty persists regarding the accuracy of representing cluster wakes via mesoscale models and their wind turbine parameterizations. Here, we evaluate the accuracy of the Fitch wind farm parameterization in the Weather Research and Forecasting model in capturing cluster-wake effects using two different options to represent turbulent mixing in the planetary boundary layer. To this end, we compare operational data from an offshore wind farm in the North Sea that is fully or partially waked by an upstream array against high-resolution mesoscale simulations. In general, we find that mesoscale models accurately represent the effect of cluster wakes on front-row turbines of a downstream wind farm. However, the same models may not accurately capture cluster-wake effects on an entire downstream wind farm, due to misrepresenting internal-wake effects.

17 WIND ENERGY↗
DOE OSTI2014

Guide for 3D WARP simulations of hollow electron beam lenses. Practical explanation on basis of Tevatron electron lens test stand

The purpose of this guide is to help successive students handle WARP. It outlines the installation of WARP on personal computers as well as super-computers and clusters. It furthermore teaches the reader how to handle the WARP environment and run basic scripts. Lastly it outlines how to execute the current Hollow Electron Beam Lens scripts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

psst: An open-source power system simulation toolbox in Python

This paper is an overview of Power System Simulation Toolbox (psst). psst is an open-source Python application for the simulation and analysis of power system models. psst simulates the wholesale market operation by solving a DC Optimal Power Flow (DCOPF), Security Constrained Unit Commitment (SCUC) and a Security Constrained Economic Dispatch (SCED). psst also includes models for the various entities in a power system such as Generator Companies (GenCos), Load Serving Entities (LSEs) and an Independent System Operator (ISO). psst features an open modular object oriented architecture that will make it useful for researchers to customize, expand, experiment beyond solving traditional problems. psst also includes a web based Graphical User Interface (GUI) that allows for user friendly interaction and for implementation on remote High Performance Computing (HPCs) clusters for parallelized operations. This paper also provides an illustrative application of psst and benchmarks with standard IEEE test cases to show the advanced features and the performance of toolbox.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Active matter shepherding and clustering in inhomogeneous environments

Here, we consider a mixture of active and passive run-and-tumble disks in an inhomogeneous environment where only half of the sample contains quenched disorder or pinning. The disks are initialized in a fully mixed state of uniform density. We identify several distinct dynamical phases as a function of motor force and pinning density. At high pinning densities and high motor forces, there is a two-step process initiated by a rapid accumulation of both active and passive disks in the pinned region, which produces a large density gradient in the system. This is followed by a slower species phase separation process where the inactive disks are shepherded by the active disks into the pin-free region, forming a nonclustered fluid and producing a more uniform density with species phase separation. For higher pinning densities and low motor forces, the dynamics becomes very slow and the system maintains a strong density gradient. For weaker pinning and large motor forces, a floating clustered state appears, and the time-averaged density of the system is uniform. We illustrate the appearance of these phases in a dynamic phase diagram.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Convenient analytical formula for cluster mean diameter and diameter dispersion after nucleation burst

Here, we propose an alternative method of estimating the mean diameter and dispersion of clusters of particles, formed in a cooling gas, right after the nucleation stage. Using a moment model developed by Friedlander [S. K. Friedlander, Ann. N. Y. Acad. Sci. 404, 354 (1983)], we derive an analytic relationship for both cluster mean diameter and diameter dispersion as a function of two of the characteristic times of the system: the cooling time and the primary constituents collision time. These formulas can be used to predict diameter and dispersion variation with process parameters, such as the initial primary constituents' concentration or cooling rate. It is also possible to use them as an input to the coagulation stage, without the need to compute complex cluster generation during the nucleation burst. We compared our results with a nodal code (NGDE) and got excellent agreement.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit(CPU)on the order of ~30 s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗
NASA NTRSDate not supplied

Estimation of 3D Woven Design Sensitivities Using a Rapid Multiscale Analysis Technique

Highly-refined finite element models of three-dimension (3D) woven composite systems currently require excessive computational demands that limit their use in sensitivity analysis, uncertainty quantification, and optimization. An alternative analysis methodology was developed using the NASA Multiscale Analysis Tool (NASMAT) where multiscale models of a 3D woven composite (including inter-tow matrix voids and constituent failure) can be completed on a single central processing unit (CPU) on the order of ~30s. To develop inputs and validation data for the NASMAT model, coupon and acid-digesting testing and x-ray computed tomography were performed. The NASMAT inputs were parameterized using a set of 25 input variables and distributions. These inputs were randomly sampled to generate a total of 100,000 NASMAT analyses that could be used to understand the influence of different material and geometric properties on the warp and weft-direction stiffness and strength. These analyses (including pre/post-processing) were performed in less than eight hours on a 120 CPU cluster. The computational efficiency of the NASMAT model enabled a sensitivity analysis to be performed, and dominant input variables were able to be identified. Key results were consistent with theoretical and experimental observations for the specific 3D woven system studied in this work.

NASMAT↗
DOE OSTI2024

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1998

The Application of Beowulf-Class Computing to Computational Electromagnetics

Current computational developments at the Jet Propulsion Laboratory (JPL) are motivated by the NASA/JPL goal of reducing payload in future space missions while increasing mission capability through miniaturization of active and passive sensors, analytical instruments and communication systems.

beowulf-class↗
DOE OSTI2017

Flexible, cluster-based analysis of the electronic medical record of sepsis with composite mixture models

The widespread adoption of electronic medical records (EMRs) in healthcare has provided vast new amounts of data for statistical machine learning researchers in their efforts to model and predict patient health status, potentially enabling novel advances in treatment. In the case of sepsis, a debilitating, dysregulated host response to infection, extracting subtle, uncataloged clinical phenotypes from the EMR with statistical machine learning methods has the potential to impact patient diagnosis and treatment early in the course of their hospitalization. However, there are significant barriers that must be overcome to extract these insights from EMR data. First, EMR datasets consist of both static and dynamic observations of discrete and continuous-valued variables, many of which may be missing, precluding the application of standard multivariate analysis techniques. Second, clinical populations observed via EMRs and relevant to the study and management of conditions like sepsis are often heterogeneous; properly accounting for this heterogeneity is critical. Here, we describe an unsupervised, probabilistic framework called a composite mixture model that can simultaneously accommodate the wide variety of observations frequently observed in EMR datasets, characterize heterogeneous clinical populations, and handle missing observations. In conclusion, we demonstrate the efficacy of our approach on a large-scale sepsis cohort, developing novel techniques built on our model-based clusters to track patient mortality risk over time and identify physiological trends and distinct subgroups of the dataset associated with elevated risk of mortality during hospitalization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Synthase-Selective Exploration of a Tunicate Microbiome by Activity-Guided Single-Cell Genomics

While thousands of environmental metagenomes have been mined for the presence of novel biosynthetic gene clusters, such computational predictions do not provide evidence of their in vivo biosynthetic functionality. Using fluorescent in situ enzyme assay targeting carrier proteins common to polyketide (PKS) and nonribosomal peptide synthetases (NRPS), we applied fluorescence-activated cell sorting to tunicate microbiome to enrich for microbes with active secondary metabolic capabilities. Single-cell genomics uncovered the genetic basis for a wide biosynthetic diversity in the enzyme-active cells and revealed a member of marine Oceanospirillales harboring a novel NRPS gene cluster with high similarity to phylogenetically distant marine and terrestrial bacteria. Interestingly, this synthase belongs to a larger class of siderophore biosynthetic gene clusters commonly associated with pestilence and disease. Furthermore, this demonstrates activity-guided single-cell genomics as a tool to guide novel biosynthetic discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary

Recent advancements in scientific equipment and the adaptation of electronics and the Internet of Things (IoT) in our everyday lives resulted in large and complex data production at a high rate. Making meaningful and timely knowledge discovery at a modest cost from this big data is difficult for computing power and storage limitations. Training deep learning models incrementally in a streaming setting can help us with overcoming these limitations. However, in a well-known phenomenon named catastrophic forgetting, incrementally trained models increasingly perform poorly on the past data. To mitigate catastrophic forgetting in training in a streaming setting, we propose constructing a historical summary over time and use the summary with newly arrived data during incremental training. We propose various data summarization techniques such as random sampling, micro clustering, coreset computation, and Auto Encoders to counteract catastrophic forgetting. We built a pipeline for incremental training with a historical summary for training deep learning models for streaming data. We demonstrate the effectiveness of historical summary in mitigating catastrophic forgetting using three case studies involving three different deep learning applications: an Artificial Neural Network (ANN) for classification task on MNIST dataset, a language model (RNN-LM) on the WikiText2 dataset, and a Convolutional Neural Network (CNN), ResNet50 to classify the ImageNet dataset. Through the training of the models, we observe that catastrophic forgetting is evident in ANN and CNN but not in an RNN. For the first task, our method recovers up to 47.9% lost accuracy due to catastrophic forgetting. For the third task, the historical summary recovers classification accuracy by up to 25%. For the second task, though there is not proof of catastrophic forgetting, the training performance (PPL) improves by up to 26% with historical summary.

Dash, Sajal↗